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Single-cell sequencing examines the nucleic acid sequence information from individual cells with optimized next-generation sequencing technologies, providing a higher resolution of cellular differences and a better understanding of the function of an individual cell in the context of its microenvironment. [1]
This single cell shows the process of the central dogma of molecular biology, which are all steps researchers are interested to quantify (DNA, RNA, and Protein).. In cell biology, single-cell analysis and subcellular analysis [1] refer to the study of genomics, transcriptomics, proteomics, metabolomics, and cell–cell interactions at the level of an individual cell, as opposed to more ...
Diseases with heterogeneous properties, such as cancer, also benefit from MDA-based genome sequencing's ability to study mutations in individual cells. The MDA products from a single cell have also been successfully used in array-comparative genomic hybridization experiments, which usually require a relatively large amount of amplified DNA.
Single-cell RNA sequencing (scRNA-Seq) provides the expression profiles of individual cells. Although it is not possible to obtain complete information on every RNA expressed by each cell, due to the small amount of material available, patterns of gene expression can be identified through gene clustering analyses .
TCR sequencing can be performed in on pooled cell populations (“bulk sequencing”) or single cells (“single cell sequencing”). [4] Bulk sequencing is useful to explore entire TCR repertoires - all the TCRs within an individual or a sample - and to generate comparisons between repertoires of different individuals. [4] This method can ...
Single-cell omics technologies has extended beyond the transcriptome to profile diverse physical-chemical properties at single-cell resolution, including whole genomes/exomes, DNA methylation, chromatin accessibility, histone modifications, epitranscriptome (e.g., mRNAs, microRNAs, tRNAs, lncRNAs), proteome, phosphoproteome, metabolome, and more.
Detecting differences in gene expression level between two populations is used both single-cell and bulk transcriptomic data. Specialised methods have been designed for single-cell data that considers single cell features such as technical dropouts and shape of the distribution e.g. Bimodal vs. unimodal. [23]
Many technologies exist for sequencing genomes, but they are designed to use DNA from a population of cells rather than a single cell. The primary challenge for single-cell genome sequencing is to make multiple copies of (amplify) the DNA so that there is enough material available for the sequencer, a process called whole genome amplification ...